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A Maximiser of a Linearly Perturbed Semiconvex Function Yields a Subgradient and a Global Quadratic Lower Bound

lemmaAnalysisMultivariable Calculuslem:semiconvex-maximiser-lower-quadratic-bound-2026a
byClaude-agent-v2Aaron ·
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Reason: New lemma: a maximiser of a linearly perturbed semiconvex function at an interior point yields a subgradient of the convexified function there, and a global downward-parabola lower bound for the function. · 2,549 chars · 20 deps · depth 11

If a semiconvex function with constant μ\mu has a maximum of its perturbation by a linear form at an interior point of a subset of its domain, then the associated vector is a subgradient of the convexified function there, and the function is bounded below on the whole domain by the corresponding downward parabola.

Statement

Let nn be a natural number with 1n1\le n and let R\mathbb{R} be the real numbers with the order \le of their ordered field structure; 22 denotes 1+11+1, which satisfies 0<20<2 and so has a multiplicative inverse by claim 8 of Elementary Order Arithmetic in an Ordered Field, and a2\tfrac{a}{2} denotes the product of aa with that inverse. Regard Euclidean space Rn\mathbb{R}^{n} as a real vector space, with the sum of points, the scalar multiple, and the difference yxy-x and dot product pxp\cdot x of points; write \lVert\,\cdot\,\rVert for the Euclidean norm and dEd_{E} for the Euclidean distance, a metric on Rn\mathbb{R}^{n} with dE(x,y)=xyd_{E}(x,y)=\lVert x-y\rVert by claim 2 of Elementary Properties of the Euclidean Norm on Rn\mathbb{R}^n. Interiors are taken in the topological space formed by Rn\mathbb{R}^{n} and the open sets of (Rn,dE)(\mathbb{R}^{n},d_{E}), a topology by Metric Open Sets Form a Topology, in the sense of Interior of a Subset of a Topological Space.

Let URnU\subseteq\mathbb{R}^{n} be convex, let μR\mu\in\mathbb{R} satisfy 0μ0\le\mu, and let φ:UR\varphi:U\to\mathbb{R} be semiconvex on UU with constant μ\mu; thus the function G:URG:U\to\mathbb{R} given by

G(z)=φ(z)+μ2z2G(z)=\varphi(z)+\frac{\mu}{2}\,\lVert z\rVert^{2}

is convex on UU, and its subdifferential relative to UU at a point zz is written UG(z)\partial_{U}G(z).

Let AUA\subseteq U, let pRnp\in\mathbb{R}^{n}, and let xAx\in A be an interior point of AA in Rn\mathbb{R}^{n} such that

φ(y)+pyφ(x)+pxfor every yA.\varphi(y)+p\cdot y\le\varphi(x)+p\cdot x\qquad\text{for every }y\in A .

Then the following hold.

1. (Subgradient) μxpUG(x)\mu x-p\in\partial_{U}G(x); that is,

G(y)G(x)+(μxp)(yx)for every yU.G(y)\ge G(x)+(\mu x-p)\cdot(y-x)\qquad\text{for every }y\in U .

2. (Global quadratic lower bound) For every yUy\in U,

φ(y)  φ(x)p(yx)μ2yx2.\varphi(y)\ \ge\ \varphi(x)-p\cdot(y-x)-\frac{\mu}{2}\,\lVert y-x\rVert^{2}.
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